Journal of the Korean Institute of Plant Engineering (대한설비관리학회지)

The Korean Institute of Plant Engineering (대한설비관리학회)
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A Study on Improving Efficiency of Recommendation System Using RFM

RFM을 활용한 추천시스템 효율화 연구

  • Jeong, Sora (Department of Applied Statistics, Korea University) ;
  • Jin, Seohoon (Division of Economics and Statistics, Korea University)
  • 정소라 (고려대학교 응용통계학과) ;
  • 진서훈 (고려대학교 경제통계학부)
  • Received : 2018.12.06
  • Accepted : 2018.12.28
  • Published : 2018.12.31
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Abstract

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

Keywords

Acknowledgement

Supported by : Korea University

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